class_docstr: Convert a collection of raw documents to a matrix of text-index
  features
  
  keep the order of word in document
  index start with 1, index 0 is default padding_value
  
  Parameters
  ----------
  
  embedding_size: size of embedding word to vector
code: def get_vocab_size(self):
      # Add 2 tokens for
      # - padding_value (index 0)
      # - unknown_value (index 1)
      return len(self.vocab) + self.ns
  
file_path: underthesea/underthesea/transformer/word_vector.py
imports: from functools import reduce
  from underthesea.transformer import Transformer
label_desc: Use image node_id 2 for calling the WordVectorTransformer obj's underlying member method code with example usage: obj.get_vocab_size() and returns: unknown